Question

State and explain the different kinds of Correlation.

Answer 1

Type I:
Based on the direction of change of variables:
Correlation is classified into two types as Positive correlation and Negative Correlation based on the direction of change of the variables.

Positive Correlation:
The correlation is said to be positive if the values of two variables move in the same direction.

Ex 1:
If income and Expenditure of a Household may be increasing or decreasing simultaneously. If so, there is a positive correlation. Ex Y = a + bx

Negative Correlation:
The Correlation is said to be negative when the values of variables move in the opposite directions. Ex Y = a – bx

Ex 1:
Price and demand for a commodity move in the opposite direction.

Type II:
Based upon the number of variables studied
There are three types based upon the number of variables studied as

1. Simple Correlation
2. Multiple Correlation
3. Partial Correlation

Simple Correlation:
If only two variables are taken for study then it is said to be a simple correlation. Ex Y = a + bx

Multiple Correlations :
If three or more three variables are studied simultaneously, then it is termed as multiple correlations.

Ex: Determinants of Quantity demanded
Qd = f (P, Pc, Ps, t, y)
Where Qd stands for Quantity demanded, f stands for function.
P is the price of the goods,
P_c is the price of competitive goods
P_s is the price of substituting goods
t is the taste and preference
y is the income.

Partial Correlation:
If there are more than two variables but only two variables are considered keeping the other variables constant, then the correlation is said to be Partial Correlation.

Type III: Based upon the constancy of the ratio of change between the variables

Correlation is divided into two types as linear correlation and Non – Linear correlation based upon the Constancy of the ratio of change between the variables.

Linear Correlation:
Correlation is said to be linear when the amount of change in one variable tends to bear a constant ratio to the amount of change in the other.
Ex Y = a + bx

Non Linear:
The correlation would be non-linear if the amount of change in one variable does not bear a constant ratio to the amount of change in the other variables.
Ex Y = a + bx²